Principal Embedded Engineer, AI Solutions

Principal Embedded Engineer, AI Solutions

Position Overview

Autodesk Technical Advisory is seeking a highly technical and customer-focused Embedded Engineer, AI Solutions to join our new Embedded Engineering (forward-deployed engineering) team.

You will work directly alongside strategic Autodesk customers to discover high-value workflows, model the data those workflows depend on, and build the AI systems for their Design & Make workflows to enable customer’s measurable business outcomes.

You will move fluidly from understanding a customer's workflow and architecture to writing code. You will spend your weeks writing production code to build agents and orchestration, retrieval and graph services, connectors and APIs - inside customer environments, alongside customer engineers. What you ship is concrete: AI agents, MCP servers and tools, domain and semantic models, knowledge-graph extensions, retrieval and context-resolution pipelines, integrations into ERP, PLM, BIM, enterprise data platforms, and eval harnesses to move new capabilities into production.

Unlike traditional project delivery, Embedded Engineering works deeply inside customer environments, connecting Autodesk technology and data with the rest of the enterprise. Insights developed through customer work will be translated into reusable agents, connectors, reference architectures, engineering patterns, and direct product feedback that shapes Autodesk's platform roadmap.

The successful candidate combines strong software and AI engineering skills with curiosity, customer empathy, business judgment, and the ability to operate independently when the problem is not yet well defined. This role is engagement-driven with travel up to 75% during active engagements, including extended periods working onsite with customers.

Not all strong candidates will meet every single qualification as listed below.

Responsibilities

Discover Workflows and Own the Outcome

• Partner with customer executives, business leaders, and technical teams to translate transformation objectives, operational KPIs, sustainability commitments, and digital initiatives into measurable, AI-enabled workflow outcomes.

• Lead co-innovation with customers to find high-value workflows, decision bottlenecks, data requirements, and integration opportunities, turning ambiguous business challenges into clear technical directions.

• Analyze end-to-end workflows across Autodesk and non-Autodesk systems to pinpoint friction, process inefficiency, data gaps, and the places where AI and agents genuinely change the economics.

• Own technical execution end-to-end - rapid prototype through architecture, build, integration, testing, governance, production deployment guidance, and adoption - with accountability for customer impact.

• Sequence the work and decide what not to build. Make explicit trade-offs between scope, speed, and durability, and defend them to both customer and Autodesk stakeholder

Make Data and Context AI-Ready

• Assess customer data readiness - fragmented sources, ownership, quality, lineage, metadata, governance, permissioning, and integration constraints - required to support trusted AI and agentic workflows.

• Structure and connect design intent, project, product, and asset data, along with requirements, supporting evidence, approvals, decisions, and dependencies, into trusted AI-ready data and context models that enable reasoning, retrieval, and agentic action.

• Design ontologies, semantic layers, and knowledge graphs that business users recognize and agents can act on, including entity resolution across source systems, schema mapping, and permission models that survive contact with enterprise reality.

Build and Ship Production AI

• Design and build production-grade AI-enabled workflows, agents, applications, and integrations that connect Autodesk products and platform capabilities with customers' broader enterprise technology environments.

• Build the retrieval and context layer these systems depend on - hybrid search, graph-grounded retrieval, tool interfaces, and MCP servers that expose governed enterprise context to agents safely.

• Make quality measurable. Build evaluation harnesses, golden datasets, guardrails, tracing, and observability so that agent behavior is testable before launch and monitored after it, and so that quality regressions and drift are caught in production rather than reported by users.

• Apply strong engineering practices across architecture, code quality, security, permissions, reliability, observability, testing, performance, and maintainability, adapting quickly to complex and constrained customer environments.